The relationship between serum uric acid (UA) levels and cancer risk remains controversial. Here, a two-sample Mendelian randomization analysis was performed to identify a causal effect of serum UA levels on cancer risk. Twenty-six single nucleotide polymorphisms strongly associated with serum UA levels were screened as genetic variants from large-scale meta-analysis data of a genome-wide association study of 110,347 European individuals. Genetic associations with eight common site-specific cancers were subsequently explored. A total of six Mendelian randomization methods were used to estimate the potential effect of serum UA levels on cancer risk, including random effects inverse variance weighting, fix effects inverse variance weighting, MR-Egger, median weighting, mode weighting, and simple mode analysis. Our primary random effects inverse variance weighted analysis revealed that no significant associations with cancers was found (all p > 0.05). Sensitivity analyses and additional analyses also showed similar pooled results. In conclusion, no significant causality between serum UA levels and cancer risk was evidenced.

BackgroundPrevious observational studies have shown an association between smoking and coronary artery disease (CAD) in patients with diabetes. Whether this association reflects causality remains unestablished. This study aimed to explore the causal effect of smoking on CAD in patients with diabetes.MethodsGenetic signatures for smoking were extracted from a large genome-wide association study (GWAS), consisted of up to 1.2 million participants. Four smoking phenotypes were included: smoking initiation, cigarettes per day, age at initiation of regular smoking, and smoking cessation. Genetic associations with CAD in patients with diabetes were extracted from another GWAS, which included 15,666 participants (3,968 CAD cases and 11,696 controls). The analyses were performed using the univariable and multivariable Mendelian randomization (MR) method.ResultsMR analysis revealed that smoking initiation was positively related to CAD risk in patients with diabetes (OR = 1.322, 95% CI = 1.114 – 1.568, P = 0.001), but this association was attenuated when adjusted for cardiovascular risk factors (OR = 1.212, 95% CI = 1.008 – 1.457, P = 0.041). Age at initiation of regular smoking was negatively related to CAD in patients with diabetes (OR = 0.214, 95% CI = 0.070 – 0.656, P = 0.007), but this association became insignificant when adjusted for cardiovascular risk factors.ConclusionsThis study supported the effect of smoking initiation on the risk of CAD in patients with diabetes.

Background Anticoagulant therapy is one of the important aspects of atrial fibrillation (AF) management, which can effectively reduce the formation of left atrial thrombosis (LAT) and the occurrence of embolic events. The CHA2DS2-VASc score is a commonly used risk assessment tool for embolic events, and it has guiding significance for anticoagulant therapy. However, a large number of recent studies have clearly shown that some of the markers that are not included in the score affect the formation of LAT. Objective This single-center study probed for risk markers for LAT by analyzing the clinical features of patients who experienced AF. Methods We reviewed patients with AF who had undergone a transesophageal echocardiography exam over the past 6 years and used binary logistic regression analysis to identify risk markers other than CHA2DS2-VASc score. For the risk markers found, the propensity score matching (PSM) was used to further evaluate whether it was an independent risk marker for LAT. The newly discovered markers were added to the score, and receiver operating characteristic analysis was used to evaluate whether the ability of the model to predict LAT was improved. Results A total of 2246 patients were included in the study. In total, 838 of them were anticoagulated (314 with rivaroxaban, 57 with dabigatran, and 467 with warfarin) and 30 patients (1.33%) had LAT. Regression analysis revealed abnormal uric acid metabolism (abUA) and obesity were risk markers for LAT. Further PSM analysis found that abUA was an independent risk marker for LAT. After including abUA, the CHA2DS2-VASc score was more accurate for LAT prediction (area under the curve difference is 0.0651, 95% confidence interval: 0.0247, 0.1050, Z = 3.158, P = 0.0016). Conclusions AbUA is an independent risk marker for LAT. After considering abUA, the CHA2DS2-VASc score for LAT is more accurate.